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Spatio-Temporal Scale Selection in Video Data Read chapter Read first chapter

2017 | OriginalPaper | Chapter

A Novel Convex Relaxation for Non-binary Discrete Tomography

Authors : Jan Kuske, Paul Swoboda, Stefania Petra

Published in: Scale Space and Variational Methods in Computer Vision

Publisher: Springer International Publishing

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Abstract

We present a novel convex relaxation and a corresponding inference algorithm for the non-binary discrete tomography problem, that is, reconstructing discrete-valued images from few linear measurements. In contrast to state of the art approaches that split the problem into a continuous reconstruction problem for the linear measurement constraints and a discrete labeling problem to enforce discrete-valued reconstructions, we propose a joint formulation that addresses both problems simultaneously, resulting in a tighter convex relaxation. For this purpose a constrained graphical model is set up and evaluated using a novel relaxation optimized by dual decomposition. We evaluate our approach experimentally and show superior solutions both mathematically (tighter relaxation) and experimentally in comparison to previously proposed relaxations.

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previous chapter Directional Total Generalized Variation Regularization for Impulse Noise Removal
next chapter Image Reconstruction by Multilabel Propagation
Footnotes
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Metadata
Title
A Novel Convex Relaxation for Non-binary Discrete Tomography
Authors
Jan Kuske
Paul Swoboda
Stefania Petra
Copyright Year
2017
Book
Scale Space and Variational Methods in Computer Vision

Print ISBN: 978-3-319-58770-7

Electronic ISBN: 978-3-319-58771-4

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-58771-4_19

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